7 citations · 19 across the 19 of their papers we have counts for
5 papers · 1 filter
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
David Köhler, David Rügamer, Matthias Schmid
Machine learning (ML) has seen significant growth in both popularity and importance. The high prediction accuracy of ML models is often achieved through complex black-box architect…
Factorized Structured Regression for Large-Scale Varying Coefficient Models
David Rügamer, Andreas Bender, Simon Wiegrebe +4
Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facil…
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Philipp Kopper, Simon Wiegrebe, Bernd Bischl +2
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications. Despite…
Automatic Componentwise Boosting: An Interpretable AutoML System
Stefan Coors, Daniel Schalk, Bernd Bischl +1
In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model e…
Inference for -Boosting
David Rügamer, Sonja Greven
We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as -Bo…